Malicious Users Detection in OFDM-based Cognitive Radio-Internet of Things using Machine Learning: Simulation and Performance

The Cooperative Spectrum Sensing (CSS) based Cognitive Radio-Internet of Things (CR-IoT) is an advantageous solution to address the spectrum scarcity problem by allowing the unlicensed users to share the available frequency bands. However, this system is vulnerable to malicious attacks due to degrading the detection performance. These attacks consist of the presence of Malicious Users (MUs), who aim to mislead the Fusion Center (FC) with fake or falsification sensing information. This work proposes multiple features-based Machine Learning (ML) like Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR) and Decision Tree (DT) algorithms, to identify MUs in Orthogonal Frequency Division Multiplexing (OFDM)-based CR-IoT networks. The performance of the proposed scheme is evaluated through simulations. The numerical results show that the proposed scheme achieves an enhanced security where MUs are correctly identified, a better detection gain and an enhanced system sum rate compared with other schemes.